Abstract
In an IoT environment, event correlation becomes more complex as events usually span over many interrelated sensors. This paper refines event correlations in an IoT environment, and proposes an algorithm to discover event correlations. We transform the event correlation discovery problem into a time-constrained frequent sequence mining problem. Moreover, we apply our approach in anomaly warning in a coal power plant. We have made extensive experiments to verify the effectiveness of our approach.
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Acknowledgement
National Key R&D Plan (No. 2017YFC0804406); National Natural Science Foundation of China (No. 61672042); The Program for Youth Backbone Individual, supported by Beijing Municipal Party Committee Organization Department, “Research of Instant Fusion of Multi-Source and Large-Scale Sensor Data”.
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Cao, Y., Liu, C., Han, Y. (2018). A Frequent Sequential Pattern Based Approach for Discovering Event Correlations. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds) Web Information Systems and Applications. WISA 2018. Lecture Notes in Computer Science(), vol 11242. Springer, Cham. https://doi.org/10.1007/978-3-030-02934-0_5
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DOI: https://doi.org/10.1007/978-3-030-02934-0_5
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